Desynchronizing effect of high-frequency stimulation in a generic cortical network model

Transcranial Electrical Stimulation (TCES) and Deep Brain Stimulation (DBS) are two different applications of electrical current to the brain used in different areas of medicine. Both have a similar frequency dependence of their efficiency, with the most pronounced effects around 100Hz. We apply superthreshold electrical stimulation, specifically depolarizing DC current, interrupted at different frequencies, to a simple model of a population of cortical neurons which uses phenomenological descriptions of neurons by Izhikevich and synaptic connections on a similar level of sophistication. With this model, we are able to reproduce the optimal desynchronization around 100Hz, as well as to predict the full frequency dependence of the efficiency of desynchronization, and thereby to give a possible explanation for the action mechanism of TCES.Comment: 9 pages, figs included. Accepted for publication in Cognitive Neurodynamic

Cytosine-to-Uracil Deamination by SssI DNA Methyltransferase

The prokaryotic DNA(cytosine-5)methyltransferase M.SssI shares the specificity of eukaryotic DNA methyltransferases (CG) and is an important model and experimental tool in the study of eukaryotic DNA methylation. Previously, M.SssI was shown to be able to catalyze deamination of the target cytosine to uracil if the methyl donor S-adenosyl-methionine (SAM) was missing from the reaction. To test whether this side-activity of the enzyme can be used to distinguish between unmethylated and C5-methylated cytosines in CG dinucleotides, we re-investigated, using a sensitive genetic reversion assay, the cytosine deaminase activity of M.SssI. Confirming previous results we showed that M.SssI can deaminate cytosine to uracil in a slow reaction in the absence of SAM and that the rate of this reaction can be increased by the SAM analogue 5’-amino-5’-deoxyadenosine. We could not detect M.SssI-catalyzed deamination of C5-methylcytosine (m5C). We found conditions where the rate of M.SssI mediated C-to-U deamination was at least 100-fold higher than the rate of m5C-to-T conversion. Although this difference in reactivities suggests that the enzyme could be used to identify C5-methylated cytosines in the epigenetically important CG dinucleotides, the rate of M.SssI mediated cytosine deamination is too low to become an enzymatic alternative to the bisulfite reaction. Amino acid replacements in the presumed SAM binding pocket of M.SssI (F17S and G19D) resulted in greatly reduced methyltransferase activity. The G19D variant showed cytosine deaminase activity in E. coli, at physiological SAM concentrations. Interestingly, the C-to-U deaminase activity was also detectable in an E. coli ung+ host proficient in uracil excision repair

Body Adiposity Index Utilization in a Spanish Mediterranean Population: Comparison with the Body Mass Index

BACKGROUND: Body fat content and fat distribution or adiposity are indicators of health risk. Several techniques have been developed and used for assessing and/or determining body fat or adiposity. Recently, the Body Adiposity Index (BAI), which is based on the measurements of hip circumference and height, has been suggested as a new index of adiposity. The aim of the study was to compare BAI and BMI measurements in a Caucasian population from a European Mediterranean area and to assess the usefulness of the BAI in men and women separately. RESEARCH METHODOLOGY/PRINCIPAL FINDINGS: A descriptive cross-sectional study was conducted in a Caucasian population. All participants in the study (1,726 women and 1,474 men, mean age 39.2 years, SD 10.8) were from Mallorca (Spain). Anthropometric data, including percentage of body fat mass obtained by Bioelectrical Impedance Analysis, were determined. Body Mass Index (BMI) and BAI were calculated. BAI and BMI showed a good correlation (r = 0.64, p<0.001). A strong correlation was also found between BAI and the % fat determined using BIA (r = 0.74, p<0.001), which is even stronger than the one between BMI and % fat (r = 0.54, p<0.001). However, the ROC curve analysis showed a higher accuracy for BMI than for the BAI regarding the discriminatory capacity. CONCLUSION: The BAI could be a good tool to measure adiposity due, at least in part, to the advantages over other more complex mechanical or electrical systems. Probably, the most important advantage of BAI over BMI is that weight is not needed. However, in general it seems that the BAI does not overcome the limitations of BMI

Modeling allosteric signal propagation using protein structure networks

Allosteric communication in proteins can be induced by the binding of effective ligands, mutations or covalent modifications that regulate a site distant from the perturbed region. To understand allosteric regulation, it is important to identify the remote sites that are affected by the perturbation-induced signals and how these allosteric perturbations are transmitted within the protein structure. In this study, by constructing a protein structure network and modeling signal transmission with a Markov random walk, we developed a method to estimate the signal propagation and the resulting effects. In our model, the global perturbation effects from a particular signal initiation site were estimated by calculating the expected visiting time (EVT), which describes the signal-induced effects caused by signal transmission through all possible routes. We hypothesized that the residues with high EVT values play important roles in allosteric signaling. We applied our model to two protein structures as examples, and verified the validity of our model using various types of experimental data. We also found that the hot spots in protein binding interfaces have significantly high EVT values, which suggests that they play roles in mediating signal communication between protein domains

Pattern electroretinogram (PERG) and pattern visual evoked potential (PVEP) in the early stages of Alzheimer’s disease

Alzheimer’s disease (AD) is one of the most common causes of dementia in the world. Patients with AD frequently complain of vision disturbances that do not manifest as changes in routine ophthalmological examination findings. The main causes of these disturbances are neuropathological changes in the visual cortex, although abnormalities in the retina and optic nerve cannot be excluded. Pattern electroretinogram (PERG) and pattern visual evoked potential (PVEP) tests are commonly used in ophthalmology to estimate bioelectrical function of the retina and optic nerve. The aim of this study was to determine whether retinal and optic nerve function, measured by PERG and PVEP tests, is changed in individuals in the early stages of AD with normal routine ophthalmological examination results. Standard PERG and PVEP tests were performed in 30 eyes of 30 patients with the early stages of AD. The results were compared to 30 eyes of 30 normal healthy controls. PERG and PVEP tests were recorded in accordance with the International Society for Clinical Electrophysiology of Vision (ISCEV) standards. Additionally, neural conduction was measured using retinocortical time (RCT)—the difference between P100-wave latency in PVEP and P50-wave implicit time in PERG. In PERG test, PVEP test, and RCT, statistically significant changes were detected. In PERG examination, increased implicit time of P50-wave (P < 0.03) and amplitudes reductions in P50- and N95-waves (P < 0.0001) were observed. In PVEP examination, increased latency of P100-wave (P < 0.0001) was found. A significant increase in RCT (P < 0.0001) was observed. The most prevalent features were amplitude reduction in N95-wave and increased latency of P100-wave which were seen in 56.7% (17/30) of the AD eyes. In patients with the early stages of AD and normal routine ophthalmological examination results, dysfunction of the retinal ganglion cells as well as of the optic nerve is present, as detected by PERG and PVEP tests. These dysfunctions, at least partially, explain the cause of visual disturbances observed in patients with the early stages of AD

Transcranial magnetic stimulation of the brain: What is stimulated? – A consensus and critical position paper

Copyright © 2022 The Author(s) and International Federation of Clinical Neurophysiology. Transcranial (electro)magnetic stimulation (TMS) is currently the method of choice to non-invasively induce neural activity in the human brain. A single transcranial stimulus induces a time-varying electric field in the brain that may evoke action potentials in cortical neurons. The spatial relationship between the locally induced electric field and the stimulated neurons determines axonal depolarization. The induced electric field is influenced by the conductive properties of the tissue compartments and is strongest in the superficial parts of the targeted cortical gyri and underlying white matter. TMS likely targets axons of both excitatory and inhibitory neurons. The propensity of individual axons to fire an action potential in response to TMS depends on their geometry, myelination and spatial relation to the imposed electric field and the physiological state of the neuron. The latter is determined by its transsynaptic dendritic and somatic inputs, intrinsic membrane potential and firing rate. Modeling work suggests that the primary target of TMS is axonal terminals in the crown top and lip regions of cortical gyri. The induced electric field may additionally excite bends of myelinated axons in the juxtacortical white matter below the gyral crown. Neuronal excitation spreads ortho- and antidromically along the stimulated axons and causes secondary excitation of connected neuronal populations within local intracortical microcircuits in the target area. Axonal and transsynaptic spread of excitation also occurs along cortico-cortical and cortico-subcortical connections, impacting on neuronal activity in the targeted network. Both local and remote neural excitation depend critically on the functional state of the stimulated target area and network. TMS also causes substantial direct co-stimulation of the peripheral nervous system. Peripheral co-excitation propagates centrally in auditory and somatosensory networks, but also produces brain responses in other networks subserving multisensory integration, orienting or arousal. The complexity of the response to TMS warrants cautious interpretation of its physiological and behavioural consequences, and a deeper understanding of the mechanistic underpinnings of TMS will be critical for advancing it as a scientific and therapeutic tool.Aman S. Aberra was supported by a U. S. A. National Science Foundation Graduate Research Fellowship (No. DGF 1106401). Andrea Antal has been supported by a grant of the Federal Ministry of Education and Research (BMBF) of Germany (Grant 01GP2124B) and by a grant of the Lower Saxony Ministry of Science and Culture (Grant 76251-12-7/19 ZN 3456). Marco Davare has been supported by a BBSRC responsive mode grant. Klaus Funke has been supported by a grant of the Federal Ministry of Education and Research (BMBF) of Germany (Grant 01EE1403B) as part of the German Center for Brain Stimulation (GCBS) and by the Deutsche Forschungsgemeinschaft (DFG) (Grants FU256/3-2; 122679504–SFB874). Mark Hallett is supported by the NINDS Intramural Program. Anke N. Karabanov holds a 4-year Sapere Aude Fellowship which is sponsored by the Independent Research Fund Denmark (Grant Nr. 0169-00027B). The sponsor had no direct involvement in the collection, analysis and interpretation of data and in the writing of the manuscript. Giacomo Koch has been supported by na EU grant H2020-EU.1.2.2. - FET Proactive (Neurotwin ID: 101017716). Sabine Meunier is Emeritus Research Director at INSERM, this has no direct involvement in the collection, analysis and interpretation of data and in the writing of the manuscript. Carlo Miniussi has been supported by a grant of the Caritro Foundation, Italy. Walter Paulus received grants from the Deutsche Forschungsgemeinschaft and BMBF. Angel V. Peterchev was supported by grants from the U. S. A. National Institutes of Health (Grants Nos. R01NS117405, R01NS088674, RF1MH114268, R01MH111865). Traian Popa has been supported by the Defitech Foundation and NIBS-iCog grant from the Swiss National Science Foundation. Hartwig R. Siebner holds a 5-year professorship in precision medicine at the Faculty of Health Sciences and Medicine, University of Copenhagen which is sponsored by the Lundbeck Foundation (Grant Nr. R186-2015-2138). The salary for Janine Kesselheim (PhD project) has been covered by a project grant “Biophysically adjusted state-informed cortex stimulation” (BASICS) funded by a synergy grant from Novo Nordisk Foundation (PI: Hartwig R Siebner, Interdisciplinary Synergy Program 2014; grant number NNF14OC001). Axel Thielscher has been supported by grants of the Lundbeck foundation (R118-A11308, R244-2017-196 and R313-2019-622). Yoshikazu Ugawa has been supported in part by grants from the Research Project Grant-in-aid for Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology (Grants 15H05881, 16H05322, 19H01091, 20K07866). Ulf Ziemann received grants from the German Ministry of Education and Research (BMBF), European Research Council (ERC), and German Research Foundation (DFG)

Transcranial Magnetic Stimulation Intensities in Cognitive Paradigms

BACKGROUND: Transcranial magnetic stimulation (TMS) has become an important experimental tool for exploring the brain's functional anatomy. As TMS interferes with neural activity, the hypothetical function of the stimulated area can thus be tested. One unresolved methodological issue in TMS experiments is the question of how to adequately calibrate stimulation intensities. The motor threshold (MT) is often taken as a reference for individually adapted stimulation intensities in TMS experiments, even if they do not involve the motor system. The aim of the present study was to evaluate whether it is reasonable to adjust stimulation intensities in each subject to the individual MT if prefrontal regions are stimulated prior to the performance of a cognitive paradigm. METHODS AND FINDINGS: Repetitive TMS (rTMS) was applied prior to a working memory task, either at the 'fixed' intensity of 40% maximum stimulator output (MSO), or individually adapted at 90% of the subject's MT. Stimulation was applied to a target region in the left posterior middle frontal gyrus (pMFG), as indicated by a functional magnetic resonance imaging (fMRI) localizer acquired beforehand, or to a control site (vertex). Results show that MT predicted the effect size after stimulating subjects with the fixed intensity (i.e., subjects with a low MT showed a greater behavioral effect). Nevertheless, the individual adaptation of intensities did not lead to stable effects. CONCLUSION: Therefore, we suggest assessing MT and account for it as a measure for general cortical TMS susceptibility, even if TMS is applied outside the motor domain

Frequently asked questions about chlorophyll fluorescence, the sequel

[EN] Using chlorophyll (Chl) a fluorescence many aspects of the photosynthetic apparatus can be studied, both in vitro and, noninvasively, in vivo. Complementary techniques can help to interpret changes in the Chl a fluorescence kinetics. Kalaji et al. (Photosynth Res 122: 121-158, 2014a) addressed several questions about instruments, methods and applications based on Chl a fluorescence. Here, additionalChl a fluorescence-related topics are discussed again in a question and answer format. Examples are the effect of connectivity on photochemical quenching, the correction of F-V/F-M values for PSI fluorescence, the energy partitioning concept, the interpretation of the complementary area, probing the donor side of PSII, the assignment of bands of 77 K fluorescence emission spectra to fluorescence emitters, the relationship between prompt and delayed fluorescence, potential problems when sampling tree canopies, the use of fluorescence parameters in QTL studies, the use of Chl a fluorescence in biosensor applications and the application of neural network approaches for the analysis of fluorescence measurements. The answers draw on knowledge fromdifferent Chl a fluorescence analysis domains, yielding in several cases new insights.Kalaji, H.; Schansker, G.; Brestic, M.; Bussotti, F.; Calatayud, A.; Ferroni, L.; Goltsev, V.... (2017). Frequently asked questions about chlorophyll fluorescence, the sequel. 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